Predicting research on characteristic parameters of blast-induced vibration based on optimized IGA-ELM model

doi:10.16265/j.cnki.issn1003-3033.2017.11.007

Abstract

Abstract: To predict the characteristic parameters for blasting vibration of open-mine effectively, an optimal IGA-ELM model was built on the basis of combination weighting. Before building the model, the parameters of input layer was determined in line with the influence factors of blasting vibration. And that of output layer were confirmed according to the safety regulation criterion for blasting. Then, the subjective and objective weights obtained by fuzzy analytic hierarchy process (FAHP) and entropy weight method respectively were integrated by applying the harmonic mean concept. And the weights of input layer parameters were quantified. In addition, IGA was introduced to select the input layer weights and hidden layer deviations of ELM by optimization. The optimal node number of ELM hidden layer was explored by using the stepwise increase-decrease method.The model was applied to a certain open-mine in China.The research results show that the optimal IGA-ELM model can be used to predict the characteristic parameters for blasting vibration of strip mine more accurately, and that the mean square error, determination coefficient, and simulation error are superior to those obtained by other models.

Key words: open mine, blast-induced vibration, characteristic parameter, combination weighing, immune genetic algorithm(IGA), extreme learning machine(ELM)

CLC Number:

X932

WEN Tingxin, CHEN Xiaoyu, LIU Tianyu, LIU Xu. Predicting research on characteristic parameters of blast-induced vibration based on optimized IGA-ELM model[J]. China Safety Science Journal, 2017, 27(11): 37-42.

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